Receptive Field Input Research Articles

Dynamic Modulation of Cortical Excitability during Visual Active Sensing

SUMMARYVisual physiology is traditionally investigated by presenting stimuli with gaze held constant. However, during active viewing of a scene, information is actively acquired using systematic patterns of fixations and saccades. Prior studies suggest that during such active viewing, both nonretinal, saccade-related signals and “extra-classical” receptive field inputs modulate visual processing. This study used a set of active viewing tasks that allowed us to compare visual responses with and without direct foveal input, thus isolating the contextual eye movement-related influences. Studying nonhuman primates, we find strong contextual modulation in primary visual cortex (V1): excitability and response amplification immediately after fixation onset, transiting to suppression leading up to the next saccade. Time-frequency decomposition suggests that this amplification and suppression cycle stems from a phase reset of ongoing neuronal oscillatory activity. The impact of saccade-related contextual modulation on stimulus processing makes active visual sensing fundamentally different from the more passive processes investigated in traditional paradigms.

Surface color and predictability determine contextual modulation of V1 firing and gamma oscillations.

The integration of direct bottom-up inputs with contextual information is a core feature of neocortical circuits. In area V1, neurons may reduce their firing rates when their receptive field input can be predicted by spatial context. Gamma-synchronized (30-80 Hz) firing may provide a complementary signal to rates, reflecting stronger synchronization between neuronal populations receiving mutually predictable inputs. We show that large uniform surfaces, which have high spatial predictability, strongly suppressed firing yet induced prominent gamma synchronization in macaque V1, particularly when they were colored. Yet, chromatic mismatches between center and surround, breaking predictability, strongly reduced gamma synchronization while increasing firing rates. Differences between responses to different colors, including strong gamma-responses to red, arose from stimulus adaptation to a full-screen background, suggesting prominent differences in adaptation between M- and L-cone signaling pathways. Thus, synchrony signaled whether RF inputs were predicted from spatial context, while firing rates increased when stimuli were unpredicted from context.

Partial and Entropic Information Decompositions of a Neuronal Modulatory Interaction

Information processing within neural systems often depends upon selective amplification of relevant signals and suppression of irrelevant signals. This has been shown many times by studies of contextual effects but there is as yet no consensus on how to interpret such studies. Some researchers interpret the effects of context as contributing to the selective receptive field (RF) input about which neurons transmit information. Others interpret context effects as affecting transmission of information about RF input without becoming part of the RF information transmitted. Here we use partial information decomposition (PID) and entropic information decomposition (EID) to study the properties of a form of modulation previously used in neurobiologically plausible neural nets. PID shows that this form of modulation can affect transmission of information in the RF input without the binary output transmitting any information unique to the modulator. EID produces similar decompositions, except that information unique to the modulator and the mechanistic shared component can be negative when modulating and modulated signals are correlated. Synergistic and source shared components were never negative in the conditions studied. Thus, both PID and EID show that modulatory inputs to a local processor can affect the transmission of information from other inputs. Contrary to what was previously assumed, this transmission can occur without the modulatory inputs becoming part of the information transmitted, as shown by the use of PID with the model we consider. Decompositions of psychophysical data from a visual contrast detection task with surrounding context suggest that a similar form of modulation may also occur in real neural systems.

More Gamma More Predictions: Gamma-Synchronization as a Key Mechanism for Efficient Integration of Classical Receptive Field Inputs with Surround Predictions.

During visual stimulation, neurons in visual cortex often exhibit rhythmic and synchronous firing in the gamma-frequency (30–90 Hz) band. Whether this phenomenon plays a functional role during visual processing is not fully clear and remains heavily debated. In this article, we explore the function of gamma-synchronization in the context of predictive and efficient coding theories. These theories hold that sensory neurons utilize the statistical regularities in the natural world in order to improve the efficiency of the neural code, and to optimize the inference of the stimulus causes of the sensory data. In visual cortex, this relies on the integration of classical receptive field (CRF) data with predictions from the surround. Here we outline two main hypotheses about gamma-synchronization in visual cortex. First, we hypothesize that the precision of gamma-synchronization reflects the extent to which CRF data can be accurately predicted by the surround. Second, we hypothesize that different cortical columns synchronize to the extent that they accurately predict each other’s CRF visual input. We argue that these two hypotheses can account for a large number of empirical observations made on the stimulus dependencies of gamma-synchronization. Furthermore, we show that they are consistent with the known laminar dependencies of gamma-synchronization and the spatial profile of intercolumnar gamma-synchronization, as well as the dependence of gamma-synchronization on experience and development. Based on our two main hypotheses, we outline two additional hypotheses. First, we hypothesize that the precision of gamma-synchronization shows, in general, a negative dependence on RF size. In support, we review evidence showing that gamma-synchronization decreases in strength along the visual hierarchy, and tends to be more prominent in species with small V1 RFs. Second, we hypothesize that gamma-synchronized network dynamics facilitate the emergence of spiking output that is particularly information-rich and sparse.

Septal neurons in barrel cortex derive their receptive field input from the lemniscal pathway.

Barrel-related circuits in the somatosensory cortex of rodents process vibrissal information conveyed through the lemniscal pathway. Yet, the origin of vibrissal input to interbarrrel regions (septa) remains an unsettled issue. A recurring proposal that never received conclusive experimental support is that septa-related circuits process paralemniscal inputs conveyed through the posterior group of the thalamus. Here we show that the receptive field of septal cells is independent of paralemniscal inputs, and that septal cells derive their receptive field input from neurons in the dorsal part of the thalamic barreloids. This result provides the missing piece of evidence for a separate pathway of vibrissal information that projects to septal columns of the barrel cortex.

Septal neurons in barrel cortex derive their receptive field input from the lemniscal pathway

Barrel-related circuits in the somatosensory cortex of rodents process vibrissal information conveyed through the lemniscal pathway. Yet, the origin of vibrissal input to interbarrrel regions (septa) remains an unsettled issue. A recurring proposal that never received conclusive experimental support is that septa-related circuits process paralemniscal inputs conveyed through the posterior group of the thalamus. Here we show that the receptive field of septal cells is independent of paralemniscal inputs, and that septal cells derive their receptive field input from neurons in the dorsal part of the thalamic barreloids. This result provides the missing piece of evidence for a separate pathway of vibrissal information that projects to septal columns of the barrel cortex.

A local circuit approach to understanding integration of long-range inputs in primary visual cortex.

Integration of inputs by cortical neurons provides the basis for the complex information processing performed in the cerebral cortex. Here, we have examined how primary visual cortical neurons integrate classical and nonclassical receptive field inputs. The effect of nonclassical receptive field stimuli and, correspondingly, of long-range intracortical inputs is known to be context-dependent: the same long-range stimulus can either facilitate or suppress responses, depending on the level of local activation. By constructing a large-scale model of primary visual cortex, we demonstrate that this effect can be understood in terms of the local cortical circuitry. Each receptive field position contributes both excitatory and inhibitory inputs; however, the inhibitory inputs have greater influence when overall receptive field drive is greater. This mechanism also explains contrast-dependent modulations within the classical receptive field, which similarly switch between excitatory and inhibitory. In order to simplify analysis and to explain the fundamental mechanisms of the model, self-contained modules that capture nonlinear local circuit interactions are constructed. This work supports the notion that receptive field integration is the result of local processing within small groups of neurons rather than in single neurons.

An internal teacher for neural computation

Contextual signals might supervise the discovery of coherently varying information between cortical modules computing different functions of their receptive field input. This hypothesis is explored in two sets of computational experiments, one studying the effects on learning of long-range unidirectional contextual signals mediated by intervening processors, and the other showing contextually supervised discovery of a high-order variable in a multilayer network.

Activation Functions, Computational Goals, and Learning Rules for Local Processors with Contextual Guidance

Information about context can enable local processors to discover latent variables that are relevant to the context within which they occur, and it can also guide short-term processing. For example, Becker and Hinton (1992) have shown how context can guide learning, and Hummel and Biederman (1992) have shown how it can guide processing in a large neural net for object recognition. This article studies the basic capabilities of a local processor with two distinct classes of inputs: receptive field inputs that provide the primary drive and contextual inputs that modulate their effects. The contextual predictions are used to guide processing without confusing them with receptive field inputs. The processor's transfer function must therefore distinguish these two roles. Given these two classes of input, the information in the output can be decomposed into four disjoint components to provide a space of possible goals in which the unsupervised learning of Linsker (1988) and the internally supervised learning of Becker and Hinton (1992) are special cases. Learning rules are derived from an information-theoretic objective function, and simulations show that a local processor trained with these rules and using an appropriate activation function has the elementary properties required.

The discovery of structure by multi-stream networks of local processors with contextual guidance

We study multi-stream networks in which feature discovery and associative learning interact cooperatively at the level of the local processors. These processors select and recode the information in their receptive field (RF) inputs that is predictably related to the context within which it occurs. To enable them to do this they are provided with local contextual input in addition to their receptive field input. This input guides both learning and processing to the RF information that is related to the context, but without confounding the information that the processor transmits about the RF. We show that these nets can discover linear functions of their inputs that are predictably related across streams. They can do so whether or not these variables are the most informative within streams, and when there is no evidence within streams as to the existence of these variables. They discover the relevant variables concurrently with, and because of, discovering the predictive relations between them. Two-stage multi-stream nets can discover the XOR function when the only evidence for it is in the higher-order cross-stream statistical dependencies, and when there are no pair-wise correlations in the input either within or across streams. This problem is at the limits of the ability of these nets, however, and they do not always solve it. Various aspects of these limitations are studied. Overall the results show that performance improves as the number of streams increases, and this encourages the view that the approach may have biological relevance.

The discovery of structure by multi-stream networks of local processors with contextual guidance

We study multi-stream networks in which feature discovery and associative learning interact cooperatively at the level of the local processors. These processors select and recode the information in their receptive field (RF) inputs that is predictably related to the context within which it occurs. To enable them to do this they are provided with local contextual input in addition to their receptive field input. This input guides both learning and processing to the RF information that is related to the context, but without confounding the information that the processor transmits about the RF. We show that these nets can discover linear functions of their inputs that are predictably related across streams. They can do so whether or not these variables are the most informative within streams, and when there is no evidence within streams as to the existence of these variables. They discover the relevant variables concurrently with, and because of, discovering the predictive relations between them. Two-stage m...

Mechanism of directional selectivity of neurons with complex receptive fields in the cat visual cortex

Spatio-temporal interactions within complex receptive fields in the cat visual cortex were investigated by sequential presentation of two stationary stimuli. When two stimuli were presented in phase (on-on or off-off) in the order corresponding to preferred direction of movement, facilitation or weak inhibition of the response to the second stimulus was observed, whereas if it corresponded to zero direction of movement, the response was strongly inhibited. In the case of stimulation out of phase (on-off or off-on), in the order corresponding to the preferred direction of movement, considerable inhibition of the response to the second stimulus was observed, whereas in the opposite order, facilitation or weak inhibition was observed. The strength of interaction between different parts of the field depended on the distance between them and the duration of the interval between stimuli. Directional selectivity of "complex" neurons is thus ensured by asymmetry of spatio-temporal interactions between receptive field inputs of the same type. Interactions between inputs of different types, arising when a multiedge stimulus (bar, grating) can be used by the visual system to distinguish an object from the background and to assess changes in size of objects and the relative velocity of their movement.